The following table contains the demand from the last 10 months:MONTH ACTUAL DEMAND1267103456789a. Calculate the single exponential smoothing forecast for these data using an a of 30 and an initial forecast (F of 31. (Round youranswers to 2 decimal places)Month50Month314567b. Calculate the exponential smoothing with trend forecast for these data using and of 0:30, a 5 of 0.30, an initial trend forecast (Tof1, and an initial exponentially smoothed forecast (F3 of 30. (Round your answers to 2 decimal places)B9ExponentialSmoothing1031343335FITI373638404041e-2. Which is best?c-1. Calculate the mean absolute deviation (MAD) for the last nine months of forecasts (Round your answers to 2 decimal places)MADSingle exponential smoothing forecastExponential amoothing with trend forecastSingle exponential smoothing forecastO Exponential smoothing with trend forecast

Single exponential smoothing-Ft+1= Ft+α (At-Ft)Here, α = Smoothing Constant = 0.30At = Actual demand…

The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 31 34 a. Calculate the single exponential smoothing forecast for these data using an or of 30 and an initial forecast (Fg of 31. (Round your answers to 2 decimal places) Month 4 5 6 7 . 9 3 4 51 6. Calculate the exponential smoothing with trend forecast for these data using and of 0.30, a 6 of 0.30, an initial trend forecast (7 of 1, and an initial exponentially smoothed forecast (Fg of 30. (Round your answers to 2 decimal places) @ Exponential Smoothing 10 35 37 36 38 40 40 41 FIT et Calculate the mean absolute deviation (MAD) for the last nine months of forecasts (Round your answers to 2 decimal places) MAD Single exponendal smoothing foreca Exponential anothing with trend forecast e-2. Which is best? Single exponential smoothing forecast Exponential smoothing with send forecast

The following table contains the demand from the last 10 months: MONTH ACTUAL DEMAND 31 34 a. Calculate the single exponential smoothing forecast for these data using an or of 30 and an initial forecast (Fg of 31. (Round your answers to 2 decimal places) Month 4 5 6 7 . 9 3 4 51 6. Calculate the exponential smoothing with trend forecast for these data using and of 0.30, a 6 of 0.30, an initial trend forecast (7 of 1, and an initial exponentially smoothed forecast (Fg of 30. (Round your answers to 2 decimal places) @ Exponential Smoothing 10 35 37 36 38 40 40 41 FIT et Calculate the mean absolute deviation (MAD) for the last nine months of forecasts (Round your answers to 2 decimal places) MAD Single exponendal smoothing foreca Exponential anothing with trend forecast e-2. Which is best? Single exponential smoothing forecast Exponential smoothing with send forecast

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 29P: The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building...

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Scenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing expenditures for packaging materials with Jeff Joyner. Ben was particularly disturbed about the amount spent on corrugated boxes purchased from Southeastern Corrugated. Ben said, I dont like the salesman from that company. He comes around here acting like he owns the place. He loves to tell us about his fancy car, house, and vacations. It seems to me he must be making too much money off of us! Jeff responded that he heard Southeastern Corrugated was going to ask for a price increase to cover the rising costs of raw material paper stock. Jeff further stated that Southeastern would probably ask for more than what was justified simply from rising paper stock costs. After the meeting, Ben decided he had heard enough. After all, he prided himself on being a results-oriented manager. There was no way he was going to allow that salesman to keep taking advantage of Coastal Products. Ben called Jeff and told him it was time to rebid the corrugated contract before Southeastern came in with a price increase request. Who did Jeff know that might be interested in the business? Jeff replied he had several companies in mind to include in the bidding process. These companies would surely come in at a lower price, partly because they used lower-grade boxes that would probably work well enough in Coastal Products process. Jeff also explained that these suppliers were not serious contenders for the business. Their purpose was to create competition with the bids. Ben told Jeff to make sure that Southeastern was well aware that these new suppliers were bidding on the contract. He also said to make sure the suppliers knew that price was going to be the determining factor in this quote, because he considered corrugated boxes to be a standard industry item. Is Ben Gibson acting legally? Is he acting ethically? Why or why not?
Scenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing expenditures for packaging materials with Jeff Joyner. Ben was particularly disturbed about the amount spent on corrugated boxes purchased from Southeastern Corrugated. Ben said, I dont like the salesman from that company. He comes around here acting like he owns the place. He loves to tell us about his fancy car, house, and vacations. It seems to me he must be making too much money off of us! Jeff responded that he heard Southeastern Corrugated was going to ask for a price increase to cover the rising costs of raw material paper stock. Jeff further stated that Southeastern would probably ask for more than what was justified simply from rising paper stock costs. After the meeting, Ben decided he had heard enough. After all, he prided himself on being a results-oriented manager. There was no way he was going to allow that salesman to keep taking advantage of Coastal Products. Ben called Jeff and told him it was time to rebid the corrugated contract before Southeastern came in with a price increase request. Who did Jeff know that might be interested in the business? Jeff replied he had several companies in mind to include in the bidding process. These companies would surely come in at a lower price, partly because they used lower-grade boxes that would probably work well enough in Coastal Products process. Jeff also explained that these suppliers were not serious contenders for the business. Their purpose was to create competition with the bids. Ben told Jeff to make sure that Southeastern was well aware that these new suppliers were bidding on the contract. He also said to make sure the suppliers knew that price was going to be the determining factor in this quote, because he considered corrugated boxes to be a standard industry item. As the Marketing Manager for Southeastern Corrugated, what would you do upon receiving the request for quotation from Coastal Products?
The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The Tinkan Company produces one-pound cans for the Canadian salmon industry. Each year the salmon spawn during a 24-hour period and must be canned immediately. Tinkan has the following agreement with the salmon industry. The company can deliver as many cans as it chooses. Then the salmon are caught. For each can by which Tinkan falls short of the salmon industrys needs, the company pays the industry a 2 penalty. Cans cost Tinkan 1 to produce and are sold by Tinkan for 2 per can. If any cans are left over, they are returned to Tinkan and the company reimburses the industry 2 for each extra can. These extra cans are put in storage for next year. Each year a can is held in storage, a carrying cost equal to 20% of the cans production cost is incurred. It is well known that the number of salmon harvested during a year is strongly related to the number of salmon harvested the previous year. In fact, using past data, Tinkan estimates that the harvest size in year t, Ht (measured in the number of cans required), is related to the harvest size in the previous year, Ht1, by the equation Ht = Ht1et where et is normally distributed with mean 1.02 and standard deviation 0.10. Tinkan plans to use the following production strategy. For some value of x, it produces enough cans at the beginning of year t to bring its inventory up to x+Ht, where Ht is the predicted harvest size in year t. Then it delivers these cans to the salmon industry. For example, if it uses x = 100,000, the predicted harvest size is 500,000 cans, and 80,000 cans are already in inventory, then Tinkan produces and delivers 520,000 cans. Given that the harvest size for the previous year was 550,000 cans, use simulation to help Tinkan develop a production strategy that maximizes its expected profit over the next 20 years. Assume that the company begins year 1 with an initial inventory of 300,000 cans.
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The following table contains the demand from the last 10 months: a. Calculate the single exponential smoothing forecast for these data using an of .30 and an initial forecast (F1) of 31.b. Calculate the exponential smoothing with trend forecast for these data using an of.30, a of .30, an initial trend forecast (T1) of 1, and an initial exponentially smoothedforecast (F1) of 30.c. Calculate the mean absolute deviation (MAD) for each forecast. Which is best?
The following table shows the actual demand observed over the last 11 years: Year 1 2 3 4 5 6 7 8 9 10 11 Demand 6 8 4 7 11 7 13 12 10 13 8 This exercise contains only parts b, c, and d. Part 2 b) Using the 3-year moving average, provide the forecast from periods 4 through 12 (round your responses to one decimal place). Part 3 c) Using the 3-year weighted moving average with weights 0.10, 0.30, and 0.60, using 0.60 for the most recent period, provide the forecast from periods 4 through 12 (round your responses to two decimal places). Part 4 d) Mean absolute deviation for the forecast developed…
Two different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows: PREDICTED DEMAND Period Demand F1 F2 1 68 66 66 2 75 68 68 3 70 72 70 4 74 71 72 5 69 72 74 6 72 70 76 7 80 71 78 8 78 74 80 Click here for the Excel Data File Compute MAD for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 1 decimal place.) b. Compute the MSE for each set of forecasts. Given your results, which forecast appears to be more accurate? (Round your answers to 2 decimal places.) c. In practice, either MAD or MSE would be employed to compute forecast errors. What factors might lead a manager to choose one rather than the other? d. Compute MAPE for each data set. Which forecast appears to be more accurate? (Round your intermediate calculations and final answers to 2 decimal places.)